Ching‐Lin Shih

23 papers receiving 499 citations

Peers

Ching‐Lin Shih
Comparison fields: 5 of 81
  • Management Science and Operations Research 178
  • Rehabilitation 47
  • Statistics and Probability 61
  • Experimental and Cognitive Psychology 66
  • Computer Networks and Communications 120
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Ida Marais Australia
Karen Draney United States
Sébastien Béland Canada
Rudy Ligtvoet Germany
Jean Cardinet Switzerland
Taehoon Kang South Korea
Alyssa Counsell Canada
Michael G. Jodoin United States
Max Auerswald Germany
Anthony D. Albano United States
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Citations per field
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Citations per year

Countries citing papers authored by Ching‐Lin Shih

Since Specialization
Citations

This map shows the geographic impact of Ching‐Lin Shih's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ching‐Lin Shih with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ching‐Lin Shih more than expected).

Fields of papers citing papers by Ching‐Lin Shih

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ching‐Lin Shih. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ching‐Lin Shih. The network helps show where Ching‐Lin Shih may publish in the future.

Co-authors

The 25 scholars most cited alongside Ching‐Lin Shih, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ching‐Lin Shih Line = papers co-authored together Ching‐Lin Shih links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200976
2 201256
3 200951
4 201050
5 201044
6 201339
7 201232
8 201230
9 200628
10 201426
11 201415
12 202215
13 202014
14 201311
15 201410
16 20205
17 20144
18 20203
19 20213
20 20232

About Ching‐Lin Shih

Ching‐Lin Shih is a scholar working on Computer Networks and Communications, Management Science and Operations Research, Experimental and Cognitive Psychology, Statistics and Probability and Education, having authored 24 papers that have together received 517 indexed citations. Recurring topics across this work include Psychometric Methodologies and Testing (9 papers), Advanced Statistical Modeling Techniques (9 papers), Cognitive Abilities and Testing (4 papers), Multi-Criteria Decision Making (2 papers), Optimal Experimental Design Methods (2 papers), Advanced Statistical Methods and Models (2 papers), Child Abuse and Trauma (1 paper) and Behavioral Health and Interventions (1 paper). The work is most often cited by research in Management Science and Operations Research (178 citations), Rehabilitation (47 citations), Statistics and Probability (61 citations), Experimental and Cognitive Psychology (66 citations) and Computer Networks and Communications (120 citations). Ching‐Lin Shih has collaborated with scholars based in Taiwan, Hong Kong and United States. Frequent co-authors include Wen‐Chung Wang, Ching‐Lin Hsieh, Hsueh‐Hua Chuang, Ching‐Fan Sheu, Sheng K. Wu, Mark Wilson, Chien‐Hung Chen, Hui‐Chu Lang, Kuan‐Lin Chen and Jyun‐Hong Chen. Their work appears in journals such as Applied Psychological Measurement, Educational and Psychological Measurement, Archives of Physical Medicine and Rehabilitation, Behavior Research Methods and Clinical Rehabilitation.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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